The transfer learning performance was tested on a set of common physicochemical and biomedical properties of small organic molecules. We selected several popular datasets marked up both for classification and regression tasks. In particular, regression datasets are presented by OPERA PHYSPROP database [1]}, that contains experimental values for the following properties: water solubility, bioconcentration factor, biodegradability half-life, Henry’s Law constant, fish biotransformation half-life, octanol–air partition coefficient, soil adsorption coefficient and vapor pressure. Biochemical classification data was obtained from PubChem bioassays datasets [2]}. It included data on activities versus: 1851(2c19) - Cytochrome P450, family 2, subfamily C, polypeptide 19, 1851(2c9) - Cytochrome P450, family 2, subfamily C, polypeptide 9, 1851(3a4) - Cytochrome P450, family 3, subfamily A, polypeptide 4, 1851(1a2) - Cytochrome P450, family 1, subfamily A, polypeptide 2, 1851(2d6) - Cytochrome P450, family 2, subfamily D, polypeptide 6, isoform 2. The bioassays datasets were selected due to their endpoint’s similarity, which makes it possible to use one of them as a donor for the others. We used 1.5 million structures from ChEMBL [3]}, [4]} with the known values of octanol-water partition coefficient (logP) as a donor dataset for OPERA database and 1851(1a2) for bioassays datasets.
